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README.md

@@ -12,58 +12,57 @@ library_name: transformers
 
 # B-GPT_en_pl_sequential
 
-The B-GPT Models are bilingual GPT-2 style models. For the first half of training, this model was trained only on English data. In the second half of training, the model was trained on only {language_2} data. At the end of training, 50 % of training data seen by the model is English and 50 % is Polish. The tokenizer was trained on the same proportions of English and Polish data. 
+This is a bilingual GPT-2 style model. For the first half of training, this model was trained only on English data. In the second half of training, the model was trained on only {language_2} data. At the end of training, 50 % of training data seen by the model is English and 50 % is Polish. The tokenizer was trained on the same proportions of English and Polish data. 
 
 ## Model details:
 
-    All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
-    For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)!
-    Details for this model specifically:
+All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
+For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)!
+Details for this model specifically:
 
-    * Architecture: gpt2
-    * Parameters: 124770816
-    * Maximum sequence length: 512 tokens
-    * Training text data (raw): [XXXX]
-    * Training tokens: 12B
-    * Vocabulary size: 50000
-    * Compute cost: ~9 NVIDIA A6000 GPU hours
-    * CO2 Emission: 1.17 kg
+* Architecture: gpt2
+* Parameters: 124770816
+* Maximum sequence length: 512 tokens
+* Training text data (raw): [XXXX]
+* Training tokens: 12B
+* Vocabulary size: 50000
+* Compute cost: ~9 NVIDIA A6000 GPU hours
+* CO2 Emission: 1.17 kg
 
-    Training datasets (percentages prior to deduplication):
-    * 100.00000%: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)
+Training datasets (percentages prior to deduplication):
+* 100.00000%: [OSCAR 2021/09](https://huggingface.co/datasets/oscar-corpus/OSCAR-2109)
 
-    Checkpoints are taken at training steps: 0, 10000, 20000, 30000, 40000, 50000, 64000, 64010, 64020, 64030, 64040, 64050, 64060, 64070, 64080, 64090, 64100, 64110, 64120, 64130, 64140, 64150, 64160, 64170, 64180, 64190, 64200, 64300, 64400, 64500, 64600, 64700, 64800, 64900, 65000, 66000, 67000, 68000, 69000, 70000, 80000, 90000, 100000, 110000, 120000, 128000.
+Checkpoints are taken at training steps: 0, 10000, 20000, 30000, 40000, 50000, 64000, 64010, 64020, 64030, 64040, 64050, 64060, 64070, 64080, 64090, 64100, 64110, 64120, 64130, 64140, 64150, 64160, 64170, 64180, 64190, 64200, 64300, 64400, 64500, 64600, 64700, 64800, 64900, 65000, 66000, 67000, 68000, 69000, 70000, 80000, 90000, 100000, 110000, 120000, 128000.
 
-    ## Use This Model
+## Use This Model
 
-    Load the model:
+Load the model:
 
-    ```
-    from transformers import AutoTokenizer, AutoModel
+```
+from transformers import AutoTokenizer, AutoModel
 
-    tokenizer = AutoTokenizer.from_pretrained("B-GPT_en_pl_sequential")
-    model = AutoModel.from_pretrained("B-GPT_en_pl_sequential")
+tokenizer = AutoTokenizer.from_pretrained("B-GPT_en_pl_sequential")
+model = AutoModel.from_pretrained("B-GPT_en_pl_sequential")
 
 
-    ````
+````
 
-    Text Generation:
+Text Generation:
 
-    ```
-    from transformers import pipeline
+```
+from transformers import pipeline
 
-    pipe = pipeline("text-generation", model="B-GPT_en_pl_sequential")
+pipe = pipeline("text-generation", model="B-GPT_en_pl_sequential")
     
-    pipe("I am a")
+pipe("I am a")
 
-    ```
+```
 
-    ## Citation
+## Citation
 
-    If you use this model, please cite:
+If you use this model, please cite:
 
-    ```
+```
 
 
-    ```
-    
+```